Evaluation of Machine Learning Approaches for Hydration Heat Prediction in Energy-Efficient Cement Composites

Accurate prediction of the heat of hydration is essential for designing low-emission, durable mortars and concretes with controlled thermal behavior, as the partial replacement of Portland cement clinker with supplementary cementitious materials (SCMs) fundamentally alters hydration kinetics. Although hydration heat can be measured experimentally, such tests are often time-consuming and labor-intensive. Machine learning (ML)-based prediction methods offer a promising alternative, but identifying the most effective model is necessary before practical application. This study evaluates the performance of three ML algorithms, CatBoost, ExtraTrees, and XGBoost, in predicting the heat of hydration in energy-efficient cementitious composites containing SCMs. A dataset of 51 experimental samples was analyzed, comprising mix composition parameters (temperature, slag, fly ash content, and water-to-binder ratio) and four output variables: heat release rate and total heat released after 12, 72, and 168 h. Model performance was assessed using cross-validation and performance metrics (MAE, RMSE, MAPE, R²). All tested models showed a high level of fit (R² > 0.9 for short-term predictions). ExtraTrees demonstrated the most consistent performance, particularly for hydration heat and heat rate estimation, while XGBoost showed superior accuracy for early-age heat evolution. Residual analyses confirmed model stability and minimal bias. The results indicate that ML-based modeling can significantly reduce laboratory workload and enhance understanding of hydration behavior in low-carbon cementitious systems.